{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#10.1\n",
    "\n",
    "#对于无向图，关联矩阵中，顶点只要在边上，则dij=1；其它dij=0\n",
    "import numpy as np\n",
    "gunalianjuzheng = np.array([\n",
    "    [1,1,0,0,0,0,0,0,0],\n",
    "    [1,0,1,0,0,0,0,0,0],\n",
    "    [0,0,1,1,1,1,0,0,0],\n",
    "    [0,1,0,1,0,0,1,1,0],\n",
    "    [0,0,0,0,1,0,1,0,1],\n",
    "    [0,0,0,0,0,1,0,1,1]\n",
    "])\n",
    "linjiejuzheng = np.array([\n",
    "    [0,e1,0,e2,0,0],\n",
    "    [e1,0,e3,0,0,0],\n",
    "    [0,e3,0,e4,e5,e6],\n",
    "    [e2,0,e4,0,e7,e8],\n",
    "    [0,0,e5,e7,0,e9],\n",
    "    [0,0,e6,e8,e9,0]\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最短路径为： [1, 3, 2, 6, 5] ;最短距离为： 11\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#10.2\n",
    "\n",
    "#用轮子networkx，使用dijkstra算法\n",
    "import numpy as np\n",
    "import pylab as plt\n",
    "import networkx as nx\n",
    "#输入各个点之间的参数\n",
    "List=[(1,2,7),(1,3,3),(1,4,12),(2,3,3),(2,6,2),(3,4,8),(4,5,1),(5,6,3)]\n",
    "G=nx.Graph()      #创建无向图\n",
    "G.add_nodes_from(range(1,6))    #增加顶点\n",
    "G.add_weighted_edges_from(List)        #从列表中添加多条边\n",
    "p=nx.dijkstra_path(G,source=1,target=5,weight='weight')     #求最短路径\n",
    "d=nx.dijkstra_path_length(G,source=1,target=5,weight='weight')     #求最短距离\n",
    "print(\"最短路径为：\",p,\";最短距离为：\",d)     #打印出答案\n",
    "#画图\n",
    "G.add_nodes_from(range(1,6))        #添加顶点集合\n",
    "G.add_weighted_edges_from(List)\n",
    "pos=nx.shell_layout(G)       #将顶点分布在同心圆上\n",
    "w=nx.get_edge_attributes(G,'weight')\n",
    "nx.draw(G,pos,with_labels=True,font_weight='bold',font_size=12)    #画图（基本条件）\n",
    "nx.draw_networkx_edge_labels(G,pos,edge_labels=w)     #在图上标记权重\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最小生成树为： {(1, 3): 3, (2, 6): 2, (2, 3): 3, (4, 5): 1, (5, 6): 3}\n",
      "最小生成树长度为： 12\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#10.3\n",
    "\n",
    "#用轮子networkx，最小生成树\n",
    "import networkx as nx\n",
    "import  pylab as plt\n",
    "L=[(1,2,7),(1,3,3),(1,4,12),(2,3,3),(2,6,2),(3,4,8),(4,5,1),(5,6,3)] #邻接矩阵\n",
    "b=nx.Graph()      #构造无向图\n",
    "b.add_nodes_from(range(1,6))       #添加顶点集合\n",
    "b.add_weighted_edges_from(L)      #添加权重边\n",
    "T=nx.minimum_spanning_tree(b)       #返回可迭代对象，默认使用kruskal算法\n",
    "w=nx.get_edge_attributes(T,'weight')      #提取字典数据\n",
    "q=nx.get_edge_attributes(b,'weight')      #对原图相对点提取字典数据\n",
    "TL = sum(w.values())     #计算最小生成树的长度，字典的值为边权\n",
    "print(\"最小生成树为：\",w)\n",
    "print(\"最小生成树长度为：\",TL)\n",
    "pos=nx.shell_layout(b)       #使图像按照顶点在同心圆上分布\n",
    "plt.rc('font',family=\"SimHei\")   #使图像可以显示中文\n",
    "#画出最小生成树图像\n",
    "plt.subplot(121)\n",
    "nx.draw(T,pos,node_size=280,with_labels=True,node_color='r')\n",
    "nx.draw_networkx_edge_labels(T,pos,edge_labels=w)\n",
    "plt.title(\"最小生成树\")\n",
    "#画出原图图像进行对比\n",
    "plt.subplot(122)\n",
    "nx.draw(b,pos,node_size=280,with_labels=True,node_color='b')\n",
    "nx.draw_networkx_edge_labels(b,pos,edge_labels=q)\n",
    "plt.title(\"原赋权无向图\")\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.  2.  6.  7.  8. 11.]\n",
      " [ 2.  0.  4.  5.  6.  9.]\n",
      " [ 6.  4.  0.  1.  2.  5.]\n",
      " [ 7.  5.  1.  0.  1.  4.]\n",
      " [ 8.  6.  2.  1.  0.  3.]\n",
      " [11.  9.  5.  4.  3.  0.]]\n",
      "总里程m= [2350. 1870. 1550. 1590. 1710. 2490.] \n",
      "最小总里程mm= 1550.0 \n",
      "建设小学的位置为： [3]\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#10.4\n",
    "\n",
    "#分别求每一个村庄作为小学点时相应的总路程\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "import pylab as plt\n",
    "#导入6个村子相互之间的道路距离数据\n",
    "List=[(1,2,2),(1,3,7),(2,3,4),(2,4,6),\n",
    "      (2,5,8),(3,4,1),(3,5,3),(4,5,1),(4,6,6),(5,6,3)]\n",
    "G=nx.Graph()      #创建无向图\n",
    "G.add_nodes_from(range(1,7))     #添加顶点集合\n",
    "G.add_weighted_edges_from(List)         #从列表中添加多条边\n",
    "c=dict(nx.shortest_path_length(G,weight='weight'))       #创建最小距离函数，并返回字典类型\n",
    "d=np.zeros((6,6))     #创建零矩阵\n",
    "#计算每个地点作为小学点时学生上学最方便\n",
    "for i in range(1,7):\n",
    "      for j in range(1,7):d[i-1,j-1]=c[i][j]\n",
    "print(d)#距离矩阵\n",
    "q=np.array([100,80,60,40,70,90])       #输入每个小学的人口数据\n",
    "m=d@q         #计算行走路程，这里使用矩阵算法\n",
    "mm=m.min()      #求路程最小值\n",
    "ind=np.where(m==mm)[0]+1       #python下标从零开始，np.where返回值为元组\n",
    "print(\"总里程m=\",m,'\\n最小总里程mm=',mm,\"\\n建设小学的位置为：\",ind)      #输出\n",
    "pos=nx.shell_layout(G)       #使画图的分布点在同心圆上\n",
    "w=nx.get_edge_attributes(G,'weight')      #在图上体现各边比重\n",
    "nx.draw(G,pos,with_labels=True,font_weight='bold',font_size=12)\n",
    "nx.draw_networkx_edge_labels(G,pos,edge_labels=w)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最小生成树的长度为： 14218.8536\n",
      "最短路径为： ['L', 'K', 'X', 'Y', 'H1', 'V1', 'F2', 'J2', 'M2', 'Z2', 'A3', 'I3', 'J3', 'R3']\n",
      "最短距离为： 2795.4679\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#10.5\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import networkx as nx\n",
    "import pylab as plt\n",
    "\n",
    "a=pd.read_excel('ti6_7.xlsx')\n",
    "b=a.values\n",
    "s = list(b[:,0])      #提取顶点字符串\n",
    "x=b[:,1]\n",
    "y=b[:,2]  #提取x,y坐标\n",
    "num=b[:,3].astype(float)  #提取顶点类别\n",
    "ad=b[:,4:].astype(str)    #提取邻接顶点\n",
    "in1=np.where(num==1)[0]  #提取第1类点的编号\n",
    "in2=np.where(num==2)[0]  #提取第2类点的编号\n",
    "in3=np.where(np.isnan(num))[0]\n",
    "\n",
    "plt.plot(x[in1],y[in1],'Pk')\n",
    "for i in range(len(in1)):\n",
    "    plt.text(x[in1[i]]+10,y[in1[i]],s[in1[i]])\n",
    "plt.plot(x[in2],y[in2],'*k')\n",
    "for i in range(len(in2)):\n",
    "    plt.text(x[in2[i]]+10,y[in2[i]],s[in2[i]])\n",
    "plt.plot(x[in3],y[in3],'.k')\n",
    "for i in range(len(in3)):\n",
    "    plt.text(x[in3[i]]+10,y[in3[i]],s[in3[i]])\n",
    "c=np.zeros((95,95))\n",
    "for i in range(95):\n",
    "    tt=list(ad[i])  #转换为列表\n",
    "    tt=[t for t in tt if t!='nan']  #删除nan\n",
    "    if len(tt)==0: continue\n",
    "    for k in range(len(tt)):\n",
    "        j=s.index(tt[k])\n",
    "        c[i,j]=np.sqrt((x[i]-x[j])**2+(y[i]-y[j])**2)\n",
    "i,j=np.nonzero(c)  #提取所有边的端点编号\n",
    "for k in range(len(i)):\n",
    "    plt.plot([x[i[k]],x[j[k]]],[y[i[k]],y[j[k]]])\n",
    "\n",
    "G=nx.Graph()         #构造无向图\n",
    "G.add_nodes_from(s)  #加入顶点集\n",
    "eds=[]               #边集初始化\n",
    "for k in range(len(i)):\n",
    "    eds.append([s[i[k]],s[j[k]],c[i[k],j[k]]])\n",
    "G.add_weighted_edges_from(eds)  #加入赋权边集\n",
    "T=nx.minimum_spanning_tree(G)  #利用Kruskal算法构造最小生成树\n",
    "w=nx.get_edge_attributes(T,'weight')  #提取边权的字典数据\n",
    "LT=sum(w.values())  #求最小生成树的长度\n",
    "print('最小生成树的长度为：', round(LT,4))\n",
    "pos=dict(zip(s,b[:,[1,2]]))  #顶点坐标的字典数据\n",
    "plt.figure()\n",
    "nx.draw_networkx(T,pos,node_size=180,font_weight='bold',\n",
    "                 with_labels=True,node_color='w')\n",
    "\n",
    "p=nx.shortest_path(G,'L','R3',weight='weight')  #求最短路径\n",
    "d=nx.shortest_path_length(G,'L','R3',weight='weight')\n",
    "print('最短路径为：',p); print('最短距离为：',round(d,4))\n",
    "plt.figure()\n",
    "nx.draw_networkx(G,pos,node_size=180,font_weight='bold',\n",
    "                 with_labels=True,node_color='w')\n",
    "path_edges=list(zip(p,p[1:]))\n",
    "nx.draw_networkx_edges(G,pos,edgelist=path_edges,\n",
    "            edge_color='r',style='dashed',width=4)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0.   0.   0.   0.   0.   0.   0.   0.   0.   0. 150. 150. 150. 150.\n",
      "  150.  60.  60.  60.  60.  60.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0. 150. 150. 150. 150.\n",
      "  150.  60.  60.  60.  60.  60.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0. 150. 150. 150. 150.\n",
      "  150.  60.  60.  60.  60.  60.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0. 120. 120. 120. 120.\n",
      "  120. 160. 160. 160. 160. 160.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0. 120. 120. 120. 120.\n",
      "  120. 160. 160. 160. 160. 160.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0. 120. 120. 120. 120.\n",
      "  120. 160. 160. 160. 160. 160.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.  80.  80.  80.  80.\n",
      "   80.  40.  40.  40.  40.  40.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.  80.  80.  80.  80.\n",
      "   80.  40.  40.  40.  40.  40.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.  80.  80.  80.  80.\n",
      "   80.  40.  40.  40.  40.  40.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.  80.  80.  80.  80.\n",
      "   80.  40.  40.  40.  40.  40.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "    0.   0.   0.   0.   0.   0.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "    0.   0.   0.   0.   0.   0.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "    0.   0.   0.   0.   0.   0.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "    0.   0.   0.   0.   0.   0.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "    0.   0.   0.   0.   0.   0.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "    0.   0.   0.   0.   0.   0.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "    0.   0.   0.   0.   0.   0.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "    0.   0.   0.   0.   0.   0.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "    0.   0.   0.   0.   0.   0.]\n",
      " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
      "    0.   0.   0.   0.   0.   0.]]\n",
      "[[7, 17], [4, 13], [1, 19], [6, 14], [8, 16], [0, 15], [2, 18], [3, 10], [9, 11], [5, 12]]\n",
      "煤矿分配对应关系为：\n",
      "煤矿场编号： [2 1 1 2 2 1 1 1 2 2]\n",
      "发电厂编号： [3 2 3 2 3 2 3 1 1 1]\n",
      "总的路程为： 780\n"
     ]
    }
   ],
   "source": [
    "#10.6\n",
    "'''\n",
    "参考知乎链接：https://zhuanlan.zhihu.com/p/482996991\n",
    "经过分析，感觉做法有点奇怪，但是书中没有介绍过min_weight_matching的用法，所以无从考证\n",
    "查阅资料，多是用非线性规划的方式解决这一问题\n",
    "线性规划：https://max.book118.com/html/2017/0901/131186136.shtm\n",
    "'''\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "from networkx.algorithms.matching import min_weight_matching      #导入数据模块\n",
    "a=np.array([[150,150,150,150,150,60,60,60,60,60],\n",
    "            [150,150,150,150,150,60,60,60,60,60],\n",
    "            [150,150,150,150,150,60,60,60,60,60],\n",
    "            [120,120,120,120,120,160,160,160,160,160],\n",
    "            [120,120,120,120,120,160,160,160,160,160],\n",
    "            [120,120,120,120,120,160,160,160,160,160],\n",
    "            [80,80,80,80,80,40,40,40,40,40],\n",
    "            [80,80,80,80,80,40,40,40,40,40],\n",
    "            [80,80,80,80,80,40,40,40,40,40],\n",
    "            [80,80,80,80,80,40,40,40,40,40]])      #导入路程数据\n",
    "b=np.zeros((20,20))\n",
    "b[0:10,10:]=a\n",
    "print(b)\n",
    "G=nx.Graph(b)\n",
    "s0=min_weight_matching(G)      #返回值为（煤矿场，发电厂）的集合\n",
    "s=[sorted(w) for w in s0]\n",
    "print(s)\n",
    "L1=[x[0] for x in s];L1=np.array(L1)+1      #煤矿场编号\n",
    "L2=[x[1] for x in s];L2=np.array(L2)-9      #发电厂编号\n",
    "c=a[L1-1,L2-1]      #提取对应的路程\n",
    "d=c.sum()         #计算总的路程\n",
    "for i in range(10):      #将编号转换一下\n",
    "    if L1[i]<=5:L1[i]=1\n",
    "    else:L1[i]=2\n",
    "    if L2[i]<4:L2[i]=1\n",
    "    elif L2[i]<7:L2[i]=2\n",
    "    else:L2[i]=3\n",
    "#输出所需的值\n",
    "print(\"煤矿分配对应关系为：\\n煤矿场编号：\",L1)\n",
    "print(\"发电厂编号：\",L2)\n",
    "print(\"总的路程为：\",d)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1]\n",
      " [2]\n",
      " [2]\n",
      " [3]\n",
      " [3]\n",
      " [4]]\n",
      "[[0.025      0.025      0.875      0.025      0.025      0.025     ]\n",
      " [0.45       0.025      0.45       0.025      0.025      0.025     ]\n",
      " [0.025      0.025      0.025      0.025      0.45       0.45      ]\n",
      " [0.30833333 0.30833333 0.30833333 0.025      0.025      0.025     ]\n",
      " [0.30833333 0.30833333 0.025      0.30833333 0.025      0.025     ]\n",
      " [0.2375     0.2375     0.025      0.2375     0.2375     0.025     ]]\n",
      "最大特征值为： [1.]\n",
      "归一化特征向量为：\n",
      " [0.1876 0.1316 0.2695 0.1026 0.1692 0.1395]\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#10.7\n",
    "\n",
    "#（1）\n",
    "#利用竞赛图，排名：1，3，2，4，5，6\n",
    "#在这里给出排名的规则是：赢的数量最多的那支队伍获胜，如果赢的数量相同，则看他们之间的比赛，谁赢，谁的排名就靠前）\n",
    "\n",
    "#（2）pageRank方法\n",
    "import numpy as np\n",
    "from scipy.sparse.linalg import eigs\n",
    "import pylab as plt\n",
    "a = np.array([[0,1,0,1,1,1],\n",
    "              [0,0,0,1,1,1],\n",
    "              [1,1,0,1,0,0],\n",
    "              [0,0,0,0,1,1],\n",
    "              [0,0,1,0,0,1],\n",
    "              [0,0,1,0,0,0]])\n",
    "w = a.T\n",
    "r = np.sum(w,axis=1,keepdims=True)#图中每只球队输球有向图输入的链接数目\n",
    "print(r)\n",
    "n = w.shape[0]#总的球队数目\n",
    "d=0.85\n",
    "P=(1-d)/n+d*w/r#利用pageRank公式\n",
    "print(P)\n",
    "#pagerank值是转移概率矩阵A的转置矩阵的最大特征值所对应的归一化特征向量\n",
    "w,v=eigs(P.T,1)  #求最大特征值及对应的特征向量\n",
    "v=v/sum(v)\n",
    "v=v.real.flatten()\n",
    "print('最大特征值为：', w.real)\n",
    "print('归一化特征向量为：\\n', np.round(v,4))\n",
    "plt.bar(range(1,n+1),v, width=0.6); plt.show()#可以看到3号球队最好"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "网络直径为： 3 \n",
      "平均路径长度为： 1.5333333333333334\n",
      "各顶点的聚类系数：\n",
      "(顶点v1:1.0000); (顶点v2:0.5000); (顶点v3:0.6667); (顶点v4:1.0000); (顶点v5:0.3333); (顶点v6:0.0000); \n",
      "整个网络的聚类系数为：0.5833\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#10.8\n",
    "\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "import pylab as plt\n",
    "#导入无权网络的数据\n",
    "l=[(1,2),(1,5),(2,4),(2,3),(2,5),(3,4),(3,5),(5,6)]\n",
    "G=nx.Graph()    #创建无向图\n",
    "G.add_nodes_from(range(1,7))    #添加顶点集合\n",
    "G.add_edges_from(l)        #从列表中添加边集\n",
    "D=nx.diameter(G)       #求网络直径\n",
    "LH=nx.average_shortest_path_length(G)     #求平均路径长度\n",
    "Ci=nx.clustering(G)        #求各类顶点的聚类系数\n",
    "C=nx.average_clustering(G)       #求整个网络的聚类系数\n",
    "print(\"网络直径为：\",D,\"\\n平均路径长度为：\",LH)\n",
    "print(\"各顶点的聚类系数：\")\n",
    "for index,value in enumerate(Ci.values()):\n",
    "    print(\"(顶点v{:d}:{:.4f});\".format(index+1,value),end=' ')\n",
    "print(\"\\n整个网络的聚类系数为：{:.4f}\".format(C))\n",
    "#对简单无权网络图的各项参数进行设置，并画出该无权网络图\n",
    "pos=nx.spring_layout(G)\n",
    "w=nx.get_edge_attributes(G,'weight')\n",
    "nx.draw(G,pos,with_labels=True,font_weight='bold',font_size=12)\n",
    "nx.draw_networkx_edge_labels(G,pos,edge_labels=w)\n",
    "plt.show()\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}